Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
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Updated
Aug 7, 2024 - Python
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
Azure MLOps
💻 Learn to make machines learn so that you don't have to struggle to program them; The ultimate list
MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
Google Cloud Platform Vertex AI end-to-end workflows for machine learning operations
🏕️ Reproducible development environment
A work in progress to build out solutions in Rust for MLOPs
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
Pybind11 bindings for Whisper.cpp
MLOps Workshop using Weights and Bias (Wandb) and Github Actions.
🛠 MLOps end-to-end guide and tutorial website, using IBM Watson, DVC, CML, Terraform, Github Actions and more.
Tutorials on creating a reproducible and maintainable data science project
A simple guide to MLOps through ZenML and its various integrations.
Fast model deployment on any cloud 🚀
Source of the FSDL 2022 labs, which are at https://github.com/full-stack-deep-learning/fsdl-text-recognizer-2022-labs
Example project with a complete MLOps cycle: versioning data, generating reports on pull requests and deploying the model on releases with DVC and CML using Github Actions and IBM Watson. Part of the Engineering Final Project @ Insper
The official python package for NimbleBox. Exposes all APIs as CLIs and contains modules to make ML 🌸
This repository houses machine learning models and pipelines for predicting various diseases, coupled with an integration with a Large Language Model for Diet and Food Recommendation. Each disease prediction task has its dedicated directory structure to maintain organization and modularity.
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